LangSmith blueprint · Software & Tech
Release gate for a coding agent
Golden tasks, CI and an LLM judge decide whether a new agent version is good enough to ship.
- Steps
- 7
- Subjective questions
- 32
- Objective questions
- 32
The problem and the outcome
Problem: A prompt tweak that looks better in a demo quietly breaks real tasks in production.
Outcome: Every agent release is measured against the last one before users see it.
Architecture
How it works, step by step
- The coding agent runs against a fixed set of real tasks from a golden dataset.
- Its pull requests must pass the same CI tests and lint as human code.
- An LLM judge scores each diff on a rubric: correctness, scope and readability.
- Golden tasks with known answers keep the scoring honest across versions.
- LangSmith experiments compare the new prompt or model with the baseline.
- The release gate lets a version through only if it scores at least as well.
- After release, online evaluators keep scoring live traces for regressions.
Tech stack
- LangSmith datasets & experiments
- CI (GitHub Actions) tests and lint
- LLM judge with a rubric
- Golden tasks with known solutions
- Online evaluators
Key design decisions
- Objective checks first (tests pass, lint clean), then an LLM judge for qualities tests cannot see.
- A fixed golden dataset, so every version is measured on the same tasks.
- A release gate: ship only if the new version scores at least as well as the baseline.
Common mistakes
- Golden tasks that leak into the prompt or training data, inflating scores.
- A judge rubric so vague that scores vary run to run.
- Comparing single runs, when agent results vary; run several times and compare averages.
What to measure
- Task pass rate (tests green)
- Judge score per rubric item
- Cost and time per task
- Regressions per release
Most-asked interview questions
Subjective questions 32
1How would you evaluate a coding agent?Asked almost every time
Use a set of realistic tasks in real repositories, each with hidden tests. Measure whether the tests pass, the size and scope of the diff, and whether lint and type checks pass, then add an LLM judge or human review for readability and fit. Run each task several times because of randomness, and track cost and time.
2Design a release gate for a coding agent.Asked almost every time
Maintain a versioned dataset of real coding tasks with repositories at fixed commits and hidden tests. For each candidate agent version, run every task several times in isolated sandboxes, check results with tests, linters and type checks first, then score quality with an LLM judge against a rubric, compare against the production version with significance tests, and block release on regressions.
3How do you build a good coding task dataset?Asked almost every time
Collect real issues and pull requests from your repositories (or curated benchmarks), freeze the repo state, write or reuse tests that verify the fix, include varied task types (bug fixes, features, refactors, tests), difficulty levels and languages, and keep a hidden holdout to avoid overfitting.
4Why are execution-based checks better than judging code by reading it?Asked almost every time
Running tests, builds and type checks objectively verifies behaviour, while LLM or human reading can miss bugs or approve plausible-looking but wrong code. Judges are useful for quality aspects tests cannot capture, like readability and scope.
5Why run each evaluation more than once?Very often asked
Agent outputs vary from run to run. One run can pass or fail by luck, so compare the average pass rate (or pass@k) over several runs, with a confidence interval, before deciding one version is better.
6How do you isolate agent runs?Very often asked
Run each attempt in a fresh container or VM with the repo at the task's commit, limited network access, resource and time limits, and no access to secrets, then collect the diff, logs and test results.
7How do you handle non-determinism in results?Very often asked
Run each task several times (for example 3–5), report pass@1 and pass@k, use confidence intervals, and require differences to be statistically significant before declaring a regression.
8What metrics would you report?Very often asked
Task success rate (tests pass), pass@k, regression rate on previously solved tasks, cost and tokens per task, time per task, number of steps or tool calls, and judge scores for code quality.
9What should an LLM judge rubric cover for code?Very often asked
Correctness relative to the task, minimal and focused changes, adherence to project style and conventions, test quality, no unrelated edits, clear explanations, and security concerns, each with a defined scale and examples.
10How do you prevent benchmark contamination?Very often asked
Use tasks created after the model's training cutoff or private repositories, hold out a hidden set, rotate tasks, and watch for suspiciously perfect scores.
11How do you analyse failures?Very often asked
Categorise failed runs (did not understand task, wrong file, broke other tests, timeout, tool misuse), inspect traces in LangSmith, and prioritise fixes by frequency and impact.
12How do you run evaluations efficiently?Very often asked
Parallelise tasks across sandboxes, cache environment setup with prebuilt images, run a fast smoke subset on every change and the full suite before release, and track cost per run.
13How do you design tasks that test whether the agent reads the existing codebase?Very often asked
Use real repositories with conventions, helpers and tests already present, write tasks whose correct solution reuses existing utilities or follows established patterns, and grade both hidden tests and whether the diff duplicates code that already existed.
14How do you evaluate the agent's ability to fix failing tests without cheating?Very often asked
Run hidden tests the agent cannot see, check that it did not edit or delete tests, skip assertions or special-case inputs, and review the diff for changes outside the intended scope.
15What should a judge rubric look like?Often asked
A few specific, observable criteria with clear scales and examples: for instance, 'changes only files needed for the task (0–2)'. Ask for a reason before the score, check the judge's agreement with human reviewers, and keep the rubric versioned.
16What is pass@k?Often asked
The probability that at least one of k sampled attempts solves the task; pass@1 reflects single-attempt reliability, which matters most for user-facing agents.
17How do you compare against the production baseline?Often asked
Run both versions on the same tasks in the same environment, pair results per task, and use paired statistical tests to see if differences are real.
18How do you evaluate safety for coding agents?Often asked
Include tasks that tempt unsafe actions (deleting files, exposing secrets, disabling tests), check that the agent refuses or asks, and monitor commands executed in sandboxes.
19How do you use human review in the loop?Often asked
Sample passing and failing runs for expert review, calibrate the LLM judge against these reviews, and add new failure types to the dataset.
20What makes a dataset 'golden' for agent evaluation?Often asked
A carefully curated, versioned set of tasks with verified expected outcomes that represents what matters for your users, used as the stable reference for comparisons.
21How do you track results over time?Often asked
Store each experiment with agent version, model, prompt and dataset version in LangSmith or a results store, and chart success rate and cost across releases.
22How do you avoid overfitting prompts to the eval set?Often asked
Keep a hidden holdout set evaluated rarely, use a separate development set for iteration, and validate improvements on real user feedback.
23How do you evaluate multi-file changes?Often asked
Use tests that exercise integration across files, check that the full build and type check pass, and include judge criteria for consistent changes across the codebase.
24How do you measure the cost and speed of a coding agent alongside quality?Often asked
Record tokens, tool calls, wall-clock time and compute cost per task, report them with pass rates, and compare versions on a quality-per-cost basis so a small gain is not bought with a large cost increase.
25How do you evaluate code review comments produced by an agent?Often asked
Seed repositories with known bugs and style issues, measure how many real issues the agent finds and how many false alarms it raises, and have engineers rate whether comments are actionable.
26How do you test the agent on tasks with ambiguous requirements?Often asked
Include tasks where the right behaviour is to ask a clarifying question or state an assumption, grade whether it does so, and penalise confidently implementing the wrong interpretation.
27How do you keep the evaluation environment realistic but safe?Often asked
Run in sandboxed containers with the project's real toolchain and dependencies cached, no access to production secrets, network allow-lists for package registries, and resource and time limits per task.
28What are SWE-bench style benchmarks?Sometimes asked
Benchmarks built from real GitHub issues and pull requests where the agent must produce a patch that makes hidden tests pass, measuring practical software engineering ability.
29What should be stored for each run?Sometimes asked
The diff, test results, logs, tool calls, tokens, cost, duration, sandbox image version and judge scores.
30How do you set the release threshold?Sometimes asked
Agree on minimum success rate, maximum allowed regression on solved tasks, and cost limits with product owners, and document them in the release process.
31How do production signals complement offline evals?Sometimes asked
Track acceptance of agent changes, reverted commits, user ratings and follow-up fixes in production, feeding new tasks back into the dataset.
32How do you report evaluation results to engineering leadership?Sometimes asked
Show pass rates by task category with confidence intervals, the change against the production baseline, cost per task, notable regressions with example traces and a clear ship or hold recommendation.
Objective questions 32
1Which check should run before an LLM judge?Asked almost every time
Why: Objective checks come first.
2Why run each evaluation task several times?Asked almost every time
Why: Report pass@1 and pass@k.
3A release gate for a coding agent compares…Asked almost every time
Why: Block on regressions.
4Which check should come before an LLM judge?Very often asked
Why: Objective, cheap checks catch clear failures before spending on a judge.
5What does a release gate compare?Very often asked
Why: It blocks a release that scores worse than the current version.
6How should each agent attempt be isolated?Very often asked
Why: Avoid interference and leaks.
7pass@1 measures…Very often asked
Why: Most relevant for users.
8What prevents benchmark contamination?Very often asked
Why: Avoid memorised answers.
9A good judge rubric includes…Very often asked
Why: Defined scales with examples.
10How are regressions confirmed?Very often asked
Why: Differences must be significant.
11Which dataset is the stable reference for comparisons?Very often asked
Why: Versioned and curated.
12Hidden tests in agent evaluations prevent…Very often asked
Why: Unseen tests check real correctness.
13Editing or deleting tests to make them pass should be…Very often asked
Why: It hides the bug instead of fixing it.
14What is a golden dataset?Often asked
Why: It stays constant so versions can be compared fairly.
15How do you speed up evaluation runs?Often asked
Why: Smoke subset on every change.
16Which failure category is common?Often asked
Why: Analyse failures by type.
17Safety evaluation tasks check that the agent…Often asked
Why: Monitor sandbox commands.
18How is an LLM judge calibrated?Often asked
Why: Sample runs for experts.
19What avoids overfitting prompts to the eval set?Often asked
Why: Iterate on a dev set.
20What should be stored per run?Often asked
Why: All of it is needed for analysis.
21Multi-file changes need…Often asked
Why: Changes must fit together.
22Production signals for coding agents include…Often asked
Why: Feed tasks back into datasets.
23Comparing versions fairly includes…Often asked
Why: A small gain may not justify a big cost rise.
24Review-comment quality is measured by…Often asked
Why: Precision and recall matter for reviewers.
25On an ambiguous task, good agent behaviour is to…Often asked
Why: Clarifying avoids building the wrong thing.
26Agent evaluation sandboxes should have…Often asked
Why: Evaluations must be realistic but safe.
27SWE-bench tasks are built from…Sometimes asked
Why: Hidden tests verify patches.
28Who agrees on release thresholds?Sometimes asked
Why: Document the thresholds in the release process.
29Which tool helps inspect failed agent runs?Sometimes asked
Why: Traces show each step.
30Cost per task matters because…Sometimes asked
Why: Release gates include cost limits.
31Task variety should include…Sometimes asked
Why: Variety represents real work.
32Results for leadership should include…Sometimes asked
Why: Uncertainty and a clear decision help them act.
Track your progress through every blueprint in your dashboard.
Start learning